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The importance of the distributed generations cannot be overemphasized. This ranges from the contribution to resilience down to the energy cost efficiency advantage at the consumers&#x2019; end. The distributed generations in some localities, however, lack connection to the utility grid due to the remoteness of the generation site. Certain benefits are, however, threatened. Thus, where there is no energy price regulation policy, fluctuations in energy prices could be the order of the day. This paper, thus, focuses on the transaction price optimization of the standalone distributed generations using the game theory approach. First, blockchain technology is incorporated in the energy transaction arena to bind prosumers and their energy transactions to a common platform. Next, for electricity price prediction, the linear regression algorithm is used to obtain the fitting equation from the current transaction data stored by the blockchain network. Using the fitting equation as the objective function, the particle swarm optimization (PSO) algorithm is used to achieve the proposed energy transaction price minimization and profit maximization. Finally, the individual hourly optimization results are fitted by a decision tree algorithm for instant referencing purposes in making energy best price transaction decisions. The individual results show that it is capable of constantly updating the optimized energy price in real-time based on the subsequent transaction records updated by the blockchain network.

Adopting the Game Theory Approach in the Blockchain-Driven Pricing Optimization of Standalone Distributed Energy Generations